论文标题
基于补丁的遥感数据分类：2D-CNN，SVM和NN分类器的比较
Patch Based Classification of Remote Sensing Data: A Comparison of 2D-CNN, SVM and NN Classifiers
论文作者
论文摘要
基于像素的算法，包括背部传播神经网络（NN）和支持向量机（SVM），已广泛用于远程感知的图像分类。在过去的几年中，基于深度学习的图像分类器（例如卷积神经网络（2D-CNN））已成为这些分类器的流行替代方案。在本文中，我们将基于斑块的SVM和NN的性能与包括2D-CNN和完全连接层的深度学习算法的性能进行了比较。类似于使用图像贴片来得出进一步分类的特征的CNN，我们建议将补丁作为输入代替带有SVM和NN分类器的单个像素。两个数据集，一个多光谱和其他高光谱数据用于比较不同分类器的性能。两个数据集的结果都表明与ART 2D-CNN分类器相比，基于贴片的SVM和NN分类器的有效性。
Pixel based algorithms including back propagation neural networks (NN) and support vector machines (SVM) have been widely used for remotely sensed image classifications. Within last few years, deep learning based image classifier like convolution neural networks (2D-CNN) are becoming popular alternatives to these classifiers. In this paper, we compare performance of patch based SVM and NN with that of a deep learning algorithms comprising of 2D-CNN and fully connected layers. Similar to CNN which utilise image patches to derive features for further classification, we propose to use patches as an input in place of individual pixel with both SVM and NN classifiers. Two datasets, one multispectral and other hyperspectral data was used to compare the performance of different classifiers. Results with both datasets suggest the effectiveness of patch based SVM and NN classifiers in comparison to state of art 2D-CNN classifier.
